Reduced conversion precision prevents model fitting

[jhug12]

Describe the bug
The use of float32 precision in the X_to_numpy and y_to_numpy functions leads to numerical problems when fitting the elastic_net_cv forecaster. This reduced precision causes a ValueError in the sklearn api when computing the gram matrix

To Reproduce
Tried unsuccessfully to reproduce it using random data.

**Desktop **

• OS: Windows 10
• Python: 3.10.0
• functime: 0.9.5

Additional context
This issue seems related to the general precision problem discussed in scikit-learn#21997. A potential enhancement could be to allow configuration of precision level through an API option, improving the flexibility and applicability of the model in diverse scenarios.